Instant Lending Decisions

Abstract

A method including training a machine learning algorithm by iteratively adjusting, by a computer processor, adjusted matching parameters to increase a correlation between approval statistics of lending decisions and risk profiles. The risk profiles represent probabilities of businesses defaulting on a loan. The probabilities are derived from usage statistics of a business management application (BMA) used by the businesses. Iteratively adjusting continues until reaching a threshold correlation between the approval statistics and the lending decisions and the risk profiles. Training generates an updated machine learning algorithm. An updated risk score for a business entity is generated using a number of logins to the BMA made by the business entity.

What is claimed is: 
     
         1 . A method, comprising:
 training a machine learning algorithm by iteratively adjusting, by a computer processor, adjusted matching parameters of the machine learning algorithm to increase a correlation between approval statistics of a plurality of lending decisions and a plurality of risk profiles, wherein:
 the plurality of risk profiles represent probabilities of a plurality of businesses defaulting on a loan, the probabilities derived from usage statistics of a business management application (BMA) used by the plurality of businesses, 
 the plurality of lending decisions are received from a computing device of a first lender and represent decisions made by the first lender whether to extend the loan to the plurality of businesses based on the plurality of risk profiles, 
 iteratively adjusting continues until reaching a threshold correlation between the approval statistics and the plurality of lending decisions and the plurality of risk profiles, and 
 training generates an updated machine learning algorithm; and 
   updating a risk score of a risk profile for a business entity in the plurality of businesses to generate an updated risk score, wherein the risk score of the risk profile for the business entity is updated using a number of logins to the BMA made by the business entity.   
     
     
         2 . The method of  claim 1 , further comprising:
 executing the updated machine learning algorithm, taking as input the updated risk score, and generating as output a probability that the business entity will default on a loan.   
     
     
         3 . The method of  claim 1 , wherein:
 the usage statistics comprises at least one category selected from the group consisting of business statistics, business financial data, online banking usage statistics, accounting software trial details, marketing interaction data, general setup statistics, payroll setup statistics, customer support data, firmographics, product usage, subscription details, subscription billing details, payroll processing details, attrition details, customer statistics, pattern changes, transaction statistics, chargebacks statistics, and age statistics, and   the machine learning algorithm comprises a rule ensemble algorithm.   
     
     
         4 . The method of  claim 1 , further comprising:
 obtaining loan default statistics of the plurality of businesses;   analyzing the loan default statistics in relationship to the plurality of risk profiles to generate a second correlation; and   adjusting the machine learning algorithm to increase the second correlation.   
     
     
         5 . The method of  claim 1 , further comprising:
 providing the risk profile to the business entity,   wherein the business entity submits the risk profile to a second lender to apply for a loan.   
     
     
         6 . The method of  claim 1 , further comprising:
 extracting, using a pre-determined clustering algorithm and based on a pre-determined similarity measure, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses;   generating a loan proposal based on the cluster of similar risk profiles; and   presenting the loan proposal to at least one entity selected from the group consisting of the first lender, a second lender, and the subset of the plurality of businesses.   
     
     
         7 . The method of  claim 1 , further comprising:
 obtaining a target risk profile from a second lender;   extracting, based on the target risk profile, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses; and   presenting the cluster of similar risk profiles and the subset of the plurality of businesses to the second lender,   wherein the second lender offers a loan program to the subset of the plurality of businesses.   
     
     
         8 . A system for generating a risk profile of a business entity, comprising:
 a computer processor;   a business management application (BMA) configured to obtain and store a plurality of usage statistics of a plurality of businesses that use the BMA;   memory storing instructions executable by the processor, wherein the instructions comprise:
 a risk profile generator configured to update a risk score of a risk profile for a business entity in the plurality of businesses to generate an updated risk score, wherein the risk score of the risk profile for the business entity is updated using a number of logins to the BMA made by the business entity. 
 a machine learning algorithm configured to be trained by iteratively adjusting adjusted matching parameters of the machine learning algorithm to increase a correlation between approval statistics of a plurality of lending decisions and a plurality of risk profiles, wherein:
 the plurality of risk profiles represent probabilities of a plurality of business entities defaulting on a loan, the probabilities derived from usage statistics of a business management application (BMA) used by the plurality of business entities, 
 the plurality of lending decisions are received from a computing device of a lender and represent decisions made by the lender whether to extend the loan to the plurality of businesses based on the plurality of risk profiles, 
 iteratively adjusting continues until reaching a threshold correlation between the approval statistics and the plurality of lending decisions and the plurality of risk profiles, and 
 
   a repository configured to store the trained machine learning algorithm.   
     
     
         9 . The system of  claim 8 , wherein:
 the usage statistics comprises at least one category selected from the group consisting of business statistics, business financial data, online banking usage statistics, accounting software trial details, marketing interaction data, general setup statistics, payroll setup statistics, customer support data, firmographics, product usage, subscription details, subscription billing details, payroll processing details, attrition details, customer statistics, pattern changes, transaction statistics, chargebacks statistics, and age statistics, and   the machine learning algorithm comprises a rule ensemble algorithm.   
     
     
         10 . The system of  claim 8 , wherein the risk profile generator is further configured to:
 obtain loan default statistics of the plurality of businesses;   analyze the loan default statistics in relationship to the plurality of risk profiles to generate a second correlation; and   adjust the machine learning algorithm to increase the second correlation.   
     
     
         11 . The system of  claim 8 , wherein the risk profile generator is further configured to:
 provide the risk profile to the business entity, wherein the business entity submits the risk profile to a second lender to apply for a loan.   
     
     
         12 . The system of  claim 8 , wherein the risk profile generator is further configured to:
 extract, using a pre-determined clustering algorithm and based on a pre-determined similarity measure, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses;   generate a loan proposal based on the cluster of similar risk profiles; and   present the loan proposal to at least one entity selected from the group consisting of the first lender, a second lender, and the subset of the plurality of businesses.   
     
     
         13 . The system of  claim 8 , wherein the risk profile generator is further configured to:
 obtain a target risk profile from a second lender;   extract, based on the target risk profile, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses; and   present the cluster of similar risk profiles and the subset of the plurality of businesses to the second lender,   wherein the second lender offers a loan program to the subset of the plurality of businesses.   
     
     
         14 . The system of  claim 8 , further comprising:
 an adaptive matching analyzer configured to execute the updated machine learning algorithm, taking as input the updated risk score, and generating as output a probability that the business entity will default on a loan.   
     
     
         15 . A non-transitory computer readable medium storing instructions which, when executed by a computer processor, comprise functionality for:
 training a machine learning algorithm by iteratively adjusting, by a computer processor, adjusted matching parameters of the machine learning algorithm to increase a correlation between approval statistics of a plurality of lending decisions and a plurality of risk profiles, wherein:
 the plurality of risk profiles represent probabilities of a plurality of businesses defaulting on a loan, the probabilities derived from usage statistics of a business management application (BMA) used by the plurality of businesses, 
 the plurality of lending decisions are received from a computing device of a first lender and represent decisions made by the first lender whether to extend the loan to the plurality of businesses based on the plurality of risk profiles, 
 iteratively adjusting continues until reaching a threshold correlation between the approval statistics and the plurality of lending decisions and the plurality of risk profiles, and 
 training generates an updated machine learning algorithm; and 
   updating a risk score of a risk profile for a business entity in the plurality of businesses to generate an updated risk score, wherein the risk score of the risk profile for the business entity is updated using a number of logins to the BMA made by the business entity.   
     
     
         16 . The non-transitory computer readable medium of  claim 15 , wherein the instructions further comprise functionality for:
 executing the updated machine learning algorithm, taking as input the updated risk score, and generating as output a probability that the business entity will default on a loan.   
     
     
         17 . The non-transitory computer readable medium of  claim 15 , wherein the instructions further comprise functionality for:
 obtaining loan default statistics of the plurality of businesses;   analyzing the loan default statistics in relationship to the plurality of risk profiles to generate a second correlation; and   adjusting the machine learning algorithm to increase the second correlation.   
     
     
         18 . The non-transitory computer readable medium of  claim 15 , wherein the instructions further comprise functionality for:
 providing the risk profile to the business entity,   wherein the business entity submits the risk profile to a second lender to apply for a loan.   
     
     
         19 . The non-transitory computer readable medium of  claim 15 , wherein the instructions further comprise functionality for:
 extracting, using a pre-determined clustering algorithm and based on a pre-determined similarity measure, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses;   generating a loan proposal based on the cluster of similar risk profiles; and   presenting the loan proposal to at least one entity selected from the group consisting of the first lender, a second lender, and the subset of the plurality of businesses.   
     
     
         20 . The non-transitory computer readable medium of  claim 15 , wherein the instructions further comprise functionality for:
 obtaining a target risk profile from a second lender;   extracting, based on the target risk profile, a cluster of similar risk profiles from the plurality of risk profiles, wherein the cluster of similar risk profiles corresponds to a subset of the plurality of businesses; and   presenting the cluster of similar risk profiles and the subset of the plurality of businesses to the second lender,   wherein the second lender offers a loan program to the subset of the plurality of businesses.